Any reference to or discussion of any document, act or item of knowledge in this specification is included solely for the purpose of providing a context for the present invention. It is not suggested or represented that any of these matters or any combination thereof formed at the priority date part of the common general knowledge, or was known to be relevant to an attempt to solve any problem with which this specification is concerned.
Proposals have been made in the past for techniques for machine recognition of selected structures and objects from remote sensing data (eg. by telemetry), such as scanned image data from airborne laser scanning (ALS) or stereo-photogrammetry. From the data it is possible to extract three-dimensional point coordinate data. To avoid the cost and time of manual human interpretation of the image data, it is then necessary to automate the processing of the data in order to associate individual points in space with recognisable objects. To this end a filtering algorithm is required to classify the point data in accordance with particular features of interest. The data defining the resulting objects can then be used in existing computer-based tools for map-making, 3D modeling, land management, asset management, etc.
Examples of previous techniques are described in U.S. Pat. No. 5,296,909, U.S. Pat. No. 7,046,841 and U.S. Pat. No. 7,397,548. However, previous approaches are relatively rudimentary and can suffer from high levels of error (both false positives and false negatives), resulting in data output that is of relatively little value or requires so much post-processing human intervention that the cost and time benefit of automation is partially or completely lost.
Further, prior techniques of identifying powerlines and similar structures have generally focused on transmission line infrastructure, and so are not appropriate for the identification of distribution system powerlines. Transmission powerlines are generally not concealed in any way by vegetation and other impediments to viewing. When applied to distribution powerlines, the existing methods are computationally very slow and require significant human interaction and quality assessment of the results before they can be useful.
Moreover, prior approaches to image data interpretation to identify powerline systems and similar are generally not designed to scale to high volumes of data, such as hundreds or even thousands of kilometers per day.